Bibliographische Detailangaben
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Personen und Körperschaften:
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Harris, J R (VerfasserIn); Grunsky, E (VerfasserIn); Behnia, P (VerfasserIn); Corrigan, D (VerfasserIn) |
| Format: |
Elektronische Zeitschrift
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| Sprache: |
English
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| veröffentlicht: |
Elsevier BV, 2015 |
| Gesamtaufnahme: |
GEM2: Geo-mapping for Energy and Minerals
, Ore Geology Reviews
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| Schlagwörter: |
Prospecting;
Prospecting Techniques;
Gold;
Mapping Techniques;
Modelling;
Random Forest (Rf) Supervised Classifier;
Nunavut;
Melville Peninsula;
Zeitschrift;
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| Quelle: |
GEOSCAN
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| Zusammenfassung: |
Data- and knowledge-driven techniques are used to produce regional Au prosp...
Data- and knowledge-driven techniques are used to produce regional Au prospectivity maps of a portion of Melville Peninsula, Northern Canada using geophysical and geochemical data. These basic datasets typically exist for large portions of Canada's North and are suitable for a "greenfields" exploration programme. The data-driven method involves the use of the Random Forest (RF) supervised classifier, a relatively new technique that has recently been applied to mineral potential modelling while the knowledge-driven technique makes use of weighted-index overlay, commonly used in GIS spatial modelling studies. We use the location of known Au occurrences to train the RF classifier and calculate the signature of Au occurrences as a group from non-occurrences using the basic geoscience dataset. The RF classification outperformed the knowledge-based model with respect to prediction of the known Au occurrences. The geochemical data in general were more predictive of the known Au occurrences than the geophysical data. A data-driven approach such as RF for the production of regional Au prospectivity maps is recommended provided that a sufficient number of training areas (known Au occurrences) exist.
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